Seven-Gene Metastasis-Associated Prognostic Model for Breast Cancer

A prognostic risk model based on seven metastasis-associated genes (IGJ, CXCL14, PTGER3, RTN1, EGOT, TLR10, PANX2) developed and validated for breast cancer. The model was constructed using data from TCGA-BRCA, AURORA US Network, SCAN-B, and GEO databases via univariate Cox and LASSO regression analyses. It was authored by Yuan Yao and last updated on 2026-05-10.

Use Cases

• Predicting patient survival outcomes based on the seven-gene risk score.
• Investigating immune infiltration phenotypes (e.g., 'hot' vs. 'cold' tumors) associated with the prognostic risk groups.
• Identifying potential novel therapeutic targets, such as the under-characterized genes RTN1 and TLR10.
• Comparing somatic mutation landscapes between high-risk and low-risk patient groups.
• Validating gene expression patterns in breast cancer cell lines based on the identified prognostic genes.

Strengths

• Model is based on data from multiple authoritative sources, including TCGA-BRCA and GEO databases.
• The seven-gene signature (IGJ, CXCL14, PTGER3, RTN1, EGOT, TLR10, PANX2) was validated across independent datasets.
• Analysis includes multi-faceted validation via calibration curves, decision curve analysis, and single-cell transcriptomics.

Limitations

• The dataset is very small (48.1 KB), suggesting it contains summary results or a model description rather than raw patient-level data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for direct machine learning application.

Provenance

Source

TCGA-BRCA, AURORA US Network, SCAN-B, and GEO databases.

Collection Method

M-CA-DEGs were identified, and a prognostic risk model was constructed via univariate Cox and LASSO regression analyses.

Freshness

Last updated 2026-05-10 22:02:13; freshness should be verified.